Abstract-IEEE 802.16 and Ethernet Passive Optical Network (EPON) are two promising broadband access technologies for high-capacity wireless access networks and wired access networks, respectively. They each can be deployed to facilitate connection between the end users and the Internet but each of them suffers from some drawbacks if operating separately. To combine the bandwidth advantage of optical networks with the mobility feature of wireless communications, we propose a convergence of EPON and 802.16 networks in this paper. First, this paper starts with presenting the converged network architecture and especially the concept of virtual ONU-BS (VOB). Then, it identifies some unique research issues in this converged network. Second, the paper investigates a dynamic bandwidth allocation (DBA) scheme and its closely associated research issues. This DBA scheme takes into consideration the specific features of the converged network to enable a smooth data transmission across optical and wireless networks, and an end-toend differentiated service to user traffics of diverse QoS (Quality of Service) requirements. This QoS-aware DBA scheme supports bandwidth fairness at the VOB level and class-of-service fairness at the 802.16 subscriber station level. The simulation results show that the proposed DBA scheme operates effectively and efficiently in terms of network throughput, average/maximum delay, resource utilization, service differentiation, etc.
Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm.
As the number of vehicles continues to grow, parking spaces are at a premium in city streets. In addition, due to the lack of knowledge about street parking spaces, heuristic circling in the streets not only costs drivers' time and fuel, but also increases city congestion. In the wake of the recent trend to build convenient, green and energy-efficient smart cities, common techniques adopted by high-profile smart parking systems are reviewed, and the performance of the various approaches are compared. A mobile sensing unit has been developed as an alternative to the fixed sensing approach. It is mounted on the passenger side of a car to measure the distance from the vehicle to the nearest roadside obstacle. By extracting parked vehicles' features from the collected trace, a supervised learning algorithm has been developed to estimate roadside parking occupancy. Multiple road tests were conducted around Wheatley (Oxfordshire) and Guildford (Surrey) in the UK. In the case of accurate GPS readings, enhanced by a map matching technique, the accuracy of the system is above 90%. A quantity estimation model is derived to calculate the density of sensing units required to cover urban streets. The estimation is quantitatively compared to a fixed sensing solution. The results show that the mobile sensing approach can perform at the same level as fixed sensing solutions when accurate location information is available but substantially fewer sensors are needed compared to the fixed sensing system.
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